Prior to the background of the invention being set forth, it may be helpful to set forth definitions of certain terms that will be used hereinafter.
The term ‘location information’ or ‘movement information’ of an object or device as used herein is defined as a movement or displacement of an object relative to a reference location or displacement trajectory x(t) of said object or device.
The term ‘RF image’ as used herein is defined as an image constructed based on RF signals affected by or reflected from an imaged or scanned object, medium or scene.
Basic distance and direction tracking and measurement systems are well known in the art. These systems relate to measuring the distance of an object or device relative to a reference point or position. The tracking and measurement systems may be utilized to measure the distance movement and/or direction of the object to identify which locations the object has past. For example a surveyor's wheel, also called trundle wheel, measuring wheel or perambulator is a device used for measuring distance.
In respect to imaging and scanning procedures of scanning or sensing devices there is a need to track the distance and direction of a device relative to reference point such as an imaging or scanning device to obtain the distance or an image of a scanned object.
For accurate coherent radar imaging, the location of each antenna in an antenna array has to be known with accuracy of a wavelength of at least λ/10 where λ is the typical signal wavelength. Accurate location is important either for synthetic aperture imaging, where the antenna array is moved during imaging, or in order to merge multiple single-snapshot images into a single image of the scanned scene.
Current SAR systems use IMS (Inertial Measurements Systems) to estimate the displacement between measurements, and usually use a data driven auto focus algorithm to correct errors in the SAR measurements.
In SAR imaging, commonly applied from aircrafts, improving the accuracy of location of the antenna sensors at the time point where the measurement was taken is an important matter. The prior art solutions that are currently used to overcome such problems, rely on autofocusing of the resulting image.
Examples of commercially used autofocusing methods and additional details regarding the principle of operation of autofocusing as herein described may be found on the Internet, for example, at: http://osl.eps.hw.ac.uk/files/uploads/publications/SASSARconf_Pailhas.pdf an article by Yan Pailhas and Yvan Petillot entitled “Synthetic Aperture Imaging and Autofocus with Coherent MIMO Sonar Systems”.
Yan Pailhas and Yvan Petillot propose two MIMO autofocus techniques to estimate with great accuracy mid-water target depth, speed and orientation. All the MIMO data in their paper are computed using a full 3D realistic MIMO simulator including multipath, seabed physical models and cloud point model to compute time echoes. For the simulations the MIMO system has a central frequency of 30 kHz. It is composed of 11 transmitters (Tx) and 11 receivers (Rx) in a “L” shape configuration. The seabed elevation is simulated using fractional Brownian motion model. Simulations were run with a sandy mud seabed type.
Another autofocus techniques may be found on the Internet, for example, at: http://www.ll.mit.edu/asap/asap_99./abstract/Yegulap.pdf entitled “Minimum Entropy SAR Autofocus”, and http://www.optics.rochester.edu/workgroups/fienup/PUBLICATIONS/OL00_SARFoc MaxSharp.pdf by entitled “Synthetic-aperture radar autofocus by maximizing sharpness” by J. R. Fienup.
Fienup suggest to focus a synthetic-aperture radar image that is suffering from phase errors, a phase-error estimate is found that, when it is applied, maximizes the sharpness of the image. Closed-form expressions are derived for the gradients of a sharpness metric with respect to phase-error parameters, including both a point-by-point (nonparametric) phase function and coefficients of a polynomial expansion. Use of these expressions allows for a highly efficient gradient-search algorithm for high-order phase errors.
Another solution according to the prior art includes PGA (phase gradient autofocus), which utilizes the measurements in a more direct fashion. Examples of commercially used PGA methods and additional details regarding the principle of operation of PGA as herein described may be found on the Internet, for example, at http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1320&context=spacegrant entitled “Expansions and Discussions of the Phase Gradient Algorithm”, and https://ecopyright.ieee.org/xplore/ie-notice.html entitled “Phase gradient autofocus-a robust tool for high resolution SAR phase correction”).
A similar method is to use autofocus algorithms to estimate and correct the target velocity, for example may be found on the Internet, for example, at ([Pailhas and Petillot] “Synthetic Aperture Imaging and Autofocus with Coherent MIMO Sonar Systems”, [Atkinson 2013] “Retrospective Motion Correction”).
The prior measuring devices and methods can be less than ideal in at least some respects. The main disadvantage of applying auto-focus or target-tracking algorithms to find an antenna array location, as disclosed by the prior art solutions, is that if the resolution of imaging which is obtained from signals recorded at a single location of the array is poor, then there is inherent ambiguity between the target location and the array location, where “target” here can be any reflector the imaging algorithm may detect. Errors in the localization of targets may cause auto-focus algorithms to deviate in the estimation of location of the array or the target, such that these targets will be amplified at the expense of other targets.